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          求助：关于用frontier计算前沿生产函数出错的问题
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              <p>
               <strong class="d4pbbc-bold">
                我用frontier4.1运算了一个函数。
               </strong>
              </p>
              <strong class="d4pbbc-bold">
              </strong>
              <p>
               <strong class="d4pbbc-bold">
                这是dat文件：
               </strong>
               <br/>
               1.00        1.00    8.66    4.97    5.34     5.18     5.72    26.54    25.73    28.39    27.67    30.54    29.60    24.67    28.55    26.83    32.67       30.89       30.66
               <br/>
               1.00        2.00    8.74    4.97    5.44     5.26     5.72    27.02    26.16    28.43    28.61    31.09    30.10    24.71    29.55    27.70    32.71       32.69       30.67
               <br/>
               1.00        3.00    8.80    4.98    5.51     5.30     5.73    27.44    26.37    28.53    29.22    31.61    30.37    24.76    30.41    28.07    32.86       29.04       30.63
               <br/>
               1.00        4.00    8.94    4.98    5.58     5.37     5.75    27.78    26.70    28.62    29.96    32.11    30.86    24.76    31.16    28.80    33.08       35.11       30.63
               <br/>
               1.00        5.00    9.03    4.99    5.64     5.46     5.78    28.11    27.24    28.84    30.79    32.59    31.58    24.87    31.78    29.84    33.43       32.06       30.65
               <br/>
               1.00        6.00    9.14    5.00    5.66     5.56     5.81    28.30    27.78    29.04    31.45    32.88    32.28    25.00    32.03    30.88    33.75       25.93       31.95
               <br/>
               1.00        7.00    9.17    5.01    5.68     5.64     5.83    28.46    28.23    29.22    32.03    33.16    32.89    25.08    32.30    31.77    34.04       37.08       31.97
               <br/>
               1.00        8.00    9.21    5.00    5.71     5.68     5.83    28.59    28.42    29.17    32.46    33.31    33.12    25.04    32.65    32.27    33.99       34.45       32.61
               <br/>
               1.00        9.00    9.33    5.00    5.76     5.75     5.81    28.79    28.74    29.01    33.15    33.46    33.41    24.95    33.21    33.10    33.72       33.05       33.60
               <br/>
               1.00       10.00    9.62    5.00    5.82     5.80     5.79    29.10    29.02    28.94    33.79    33.70    33.61    24.99    33.89    33.69    33.52       37.13       32.91
               <br/>
               1.00       11.00    9.81    5.01    5.89     5.88     5.78    29.50    29.48    28.95    34.66    34.03    34.00    25.10    34.68    34.63    33.39       30.61       32.77
               <br/>
               1.00       12.00    9.86    5.03    5.95     5.95     5.78    29.93    29.89    29.03    35.41    34.40    34.36    25.26    35.46    35.37    33.37       30.84       33.06
               <br/>
               1.00       13.00    9.84    5.04    6.04     5.99     5.78    30.43    30.15    29.12    36.16    34.93    34.61    25.37    36.49    35.84    33.43       34.70       33.28
               <br/>
               1.00       14.00    9.82    5.05    6.11     6.01     5.79    30.86    30.35    29.22    36.76    35.39    34.80    25.48    37.38    36.15    33.50       32.20       33.59
               <br/>
               1.00       15.00    9.77    5.05    6.19     6.02     5.80    31.30    30.42    29.28    37.30    35.90    34.91    25.53    38.37    36.27    33.60       31.96       33.99
               <br/>
               1.00       16.00    9.74    5.05    6.26     6.03     5.80    31.65    30.45    29.30    37.76    36.33    34.95    25.52    39.25    36.33    33.63       34.99       34.43
               <br/>
               1.00       17.00    9.74    5.05    6.31     6.05     5.78    31.87    30.56    29.19    38.21    36.50    35.00    25.48    39.85    36.64    33.44       33.53       34.84
               <br/>
               1.00       18.00    9.74    5.04    6.36     6.07     5.77    32.07    30.61    29.08    38.63    36.70    35.03    25.41    40.47    36.88    33.27       30.47       35.15
               <br/>
               1.00       19.00    9.73    5.03    6.40     6.09     5.74    32.19    30.61    28.88    38.99    36.79    34.98    25.27    41.00    37.08    33.00       35.68       35.44
               <br/>
               1.00       20.00    9.85    5.03    6.46     6.14     5.72    32.54    30.90    28.81    39.68    36.99    35.14    25.34    41.78    37.69    32.76       24.16       35.48
               <br/>
               1.00       21.00    9.88    5.05    6.53     6.17     5.70    32.95    31.12    28.78    40.27    37.24    35.17    25.47    42.64    38.03    32.52       24.97       35.39
              </p>
              <p>
               <strong class="d4pbbc-bold">
                这个是ins文件
               </strong>
               <br/>
               1               1=ERROR COMPONENTS MODEL, 2=TE EFFECTS MODEL
               <br/>
               aa.txt         DATA FILE NAME
               <br/>
               aaout.txt         OUTPUT FILE NAME
               <br/>
               1               1=PRODUCTION FUNCTION, 2=COST FUNCTION
               <br/>
               y               LOGGED DEPENDENT VARIABLE (Y/N)
               <br/>
               1             NUMBER OF CROSS-SECTIONS
               <br/>
               21              NUMBER OF TIME PERIODS
               <br/>
               21              NUMBER OF OBSERVATIONS IN TOTAL
               <br/>
               16               NUMBER OF REGRESSOR VARIABLES (Xs)
               <br/>
               y              MU (Y/N) [OR DELTA0 (Y/N) IF USING TE EFFECTS MODEL]
               <br/>
               n               ETA (Y/N) [OR NUMBER OF TE EFFECTS REGRESSORS (Zs)]
               <br/>
               n               STARTING VALUES (Y/N)
               <br/>
               IF YES THEN  BETA0
               <br/>
               BETA1 TO
               <br/>
               BETAK
               <br/>
               SIGMA SQUARED
               <br/>
               GAMMA
               <br/>
               MU      [OR DELTA0
               <br/>
               ETA        DELTA1 TO
               <br/>
               DELTAK]
               <br/>
               NOTE: IF YOU ARE SUPPLYING STARTING VALUES
               <br/>
               AND YOU HAVE RESTRICTED MU [OR DELTA0] TO BE
               <br/>
               ZERO THEN YOU SHOULD NOT SUPPLY A STARTING
               <br/>
               VALUE FOR THIS PARAMETER.
              </p>
              <p>
               <strong class="d4pbbc-bold">
                这个是结果out文件：
               </strong>
               <br/>
               Output from the program FRONTIER (Version 4.1c)
              </p>
              <p>
               instruction file = i.txt
               <br/>
               data file =        aa.txt
              </p>
              <p>
               Error Components Frontier (see B&amp;C 1992)
               <br/>
               The model is a production function
               <br/>
               The dependent variable is logged
              </p>
              <p>
               the ols estimates are :
              </p>
              <p>
               coefficient     standard-error    t-ratio
              </p>
              <p>
               beta 0         0.55283909E+02  0.29740690E+02  0.18588644E+01
               <br/>
               beta 1        -0.13444607E+02  0.65357467E+01 -0.20570881E+01
               <br/>
               beta 2        -0.53925759E+01  0.36907860E+01 -0.14610915E+01
               <br/>
               beta 3        -0.35809798E+01  0.33936786E+01 -0.10551912E+01
               <br/>
               beta 4         0.77438630E+01  0.48817898E+01  0.15862754E+01
               <br/>
               beta 5        -0.51696797E+01  0.20527183E+01 -0.25184555E+01
               <br/>
               beta 6         0.14294458E+02  0.41013514E+01  0.34853042E+01
               <br/>
               beta 7        -0.10861241E+02  0.33362669E+01 -0.32555071E+01
               <br/>
               beta 8        -0.67962651E+01  0.45252150E+01 -0.15018657E+01
               <br/>
               beta 9         0.72014084E+01  0.20045514E+01  0.35925286E+01
               <br/>
               beta10        -0.10300554E+02  0.33463769E+01 -0.30781214E+01
               <br/>
               beta11         0.23066457E+01  0.19214074E+01  0.12004980E+01
               <br/>
               beta12         0.24395781E+01  0.19024666E+01  0.12823237E+01
               <br/>
               beta13         0.28961363E+01  0.23800830E+01  0.12168215E+01
               <br/>
               beta14         0.52660486E+01  0.19869789E+01  0.26502790E+01
               <br/>
               beta15         0.30972389E-03  0.34384841E-02  0.90075708E-01
               <br/>
               beta16        -0.74810801E-01  0.36037058E-01 -0.20759408E+01
               <br/>
               sigma-squared  0.90741257E-03
              </p>
              <p>
               log likelihood function =   0.61165276E+02
              </p>
              <p>
               the estimates after the grid search were :
              </p>
              <p>
               beta 0         0.55286293E+02
               <br/>
               beta 1        -0.13444607E+02
               <br/>
               beta 2        -0.53925759E+01
               <br/>
               beta 3        -0.35809798E+01
               <br/>
               beta 4         0.77438630E+01
               <br/>
               beta 5        -0.51696797E+01
               <br/>
               beta 6         0.14294458E+02
               <br/>
               beta 7        -0.10861241E+02
               <br/>
               beta 8        -0.67962651E+01
               <br/>
               beta 9         0.72014084E+01
               <br/>
               beta10        -0.10300554E+02
               <br/>
               beta11         0.23066457E+01
               <br/>
               beta12         0.24395781E+01
               <br/>
               beta13         0.28961363E+01
               <br/>
               beta14         0.52660486E+01
               <br/>
               beta15         0.30972389E-03
               <br/>
               beta16        -0.74810801E-01
               <br/>
               sigma-squared  0.17852306E-03
               <br/>
               gamma          0.50000000E-01
               <br/>
               mu             0.00000000E+00
               <br/>
               eta is restricted to be zero
              </p>
              <p>
               iteration =     0  func evals =     20  llf =  0.61000219E+02
               <br/>
               0.55286293E+02-0.13444607E+02-0.53925759E+01-0.35809798E+01 0.77438630E+01
               <br/>
               -0.51696797E+01 0.14294458E+02-0.10861241E+02-0.67962651E+01 0.72014084E+01
               <br/>
               -0.10300554E+02 0.23066457E+01 0.24395781E+01 0.28961363E+01 0.52660486E+01
               <br/>
               0.30972389E-03-0.74810801E-01 0.17852306E-03 0.50000000E-01 0.00000000E+00
               <br/>
               gradient step
               <br/>
               pt better than entering pt cannot be found
               <br/>
               iteration =     1  func evals =     28  llf =  0.61000219E+02
               <br/>
               0.55286293E+02-0.13444607E+02-0.53925759E+01-0.35809798E+01 0.77438630E+01
               <br/>
               -0.51696797E+01 0.14294458E+02-0.10861241E+02-0.67962651E+01 0.72014084E+01
               <br/>
               -0.10300554E+02 0.23066457E+01 0.24395781E+01 0.28961363E+01 0.52660486E+01
               <br/>
               0.30972389E-03-0.74810801E-01 0.17852306E-03 0.50000000E-01 0.00000000E+00
              </p>
              <p>
               the final mle estimates are :
              </p>
              <p>
               coefficient     standard-error    t-ratio
              </p>
              <p>
               beta 0         0.55286293E+02  0.10000000E+01  0.55286293E+02
               <br/>
               beta 1        -0.13444607E+02  0.10000000E+01 -0.13444607E+02
               <br/>
               beta 2        -0.53925759E+01  0.10000000E+01 -0.53925759E+01
               <br/>
               beta 3        -0.35809798E+01  0.10000000E+01 -0.35809798E+01
               <br/>
               beta 4         0.77438630E+01  0.10000000E+01  0.77438630E+01
               <br/>
               beta 5        -0.51696797E+01  0.10000000E+01 -0.51696797E+01
               <br/>
               beta 6         0.14294458E+02  0.10000000E+01  0.14294458E+02
               <br/>
               beta 7        -0.10861241E+02  0.10000000E+01 -0.10861241E+02
               <br/>
               beta 8        -0.67962651E+01  0.10000000E+01 -0.67962651E+01
               <br/>
               beta 9         0.72014084E+01  0.10000000E+01  0.72014084E+01
               <br/>
               beta10        -0.10300554E+02  0.10000000E+01 -0.10300554E+02
               <br/>
               beta11         0.23066457E+01  0.10000000E+01  0.23066457E+01
               <br/>
               beta12         0.24395781E+01  0.10000000E+01  0.24395781E+01
               <br/>
               beta13         0.28961363E+01  0.10000000E+01  0.28961363E+01
               <br/>
               beta14         0.52660486E+01  0.10000000E+01  0.52660486E+01
               <br/>
               beta15         0.30972389E-03  0.10000000E+01  0.30972389E-03
               <br/>
               beta16        -0.74810801E-01  0.10000000E+01 -0.74810801E-01
               <br/>
               sigma-squared  0.17852306E-03  0.10000000E+01  0.17852306E-03
               <br/>
               gamma          0.50000000E-01  0.10000000E+01  0.50000000E-01
               <br/>
               mu             0.00000000E+00  0.10000000E+01  0.00000000E+00
               <br/>
               eta is restricted to be zero
              </p>
              <p>
               log likelihood function =   0.61000219E+02
              </p>
              <p>
               <strong class="d4pbbc-bold">
                the likelihood value is less than that obtained
                <br/>
                using ols! – try again using different starting values
               </strong>
               <br/>
               number of iterations =      1
              </p>
              <p>
               (maximum number of iterations set at :   100)
              </p>
              <p>
               number of cross-sections =      1
              </p>
              <p>
               number of time periods =     21
              </p>
              <p>
               total number of observations =     21
              </p>
              <p>
               thus there are:      0  obsns not in the panel
              </p>
              <p>
               covariance matrix :
              </p>
              <p>
               0.10000000E+01  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.10000000E+01  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.10000000E+01  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.10000000E+01  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.10000000E+01
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.10000000E+01  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.10000000E+01  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.10000000E+01  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.10000000E+01  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.10000000E+01
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.10000000E+01  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.10000000E+01  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.10000000E+01  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.10000000E+01  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.10000000E+01
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.10000000E+01  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.10000000E+01  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.10000000E+01  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.10000000E+01  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00
               <br/>
               0.00000000E+00  0.00000000E+00  0.00000000E+00  0.00000000E+00  0.10000000E+01
              </p>
              <p>
               technical efficiency estimates :
              </p>
              <p>
               firm             eff.-est.
              </p>
              <p>
               1           0.99781453E+00
              </p>
              <p>
               mean efficiency =   0.99781453E+00
              </p>
              <p>
               summary of panel of observations:
               <br/>
               (1 = observed, 0 = not observed)
              </p>
              <p>
               t:   1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19  20  21
               <br/>
               n
               <br/>
               1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1  21
              </p>
              <p>
               1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1   1  21
              </p>
              <p>
               <strong class="d4pbbc-bold">
                请问：
                <br/>
                为什么会出现
                <br/>
                the likelihood value is less than that obtained
                <br/>
                using ols! – try again using different starting values
               </strong>
              </p>
              <strong class="d4pbbc-bold">
               <p>
                及后面的 covariance matrix  全部为零的情况？
               </p>
               <p>
                我将变量进行过好多种变换，问题仍然存在。
                <br/>
                什么情况下会发生此种问题？
                <br/>
                应该如何解决？
               </p>
              </strong>
              <p>
               <strong class="d4pbbc-bold">
                望高手不吝赐教。
               </strong>
              </p>
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              <span class="bbp-reply-post-date">
               2008年11月17日 上午3:53
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               我也出现了类似的问题,并且除了数据不同以外,其它的都一样的,并且ins文件的设置也是一样的,也正在苦恼中,不知道怎么解决,真的希望高手能名帮忙.
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